Table descriptions

After partitioning and chunking, you can have Unstructured generate text-based summaries of detected tables.

This summarization is done by using models offered through these providers:

• GPT-4o, provided through OpenAI.
• Claude 3.5 Sonnet, provided through Anthropic.
• Claude 3.5 Sonnet, provided through Amazon Bedrock.
• Gemini 2.0 Flash, provided through Vertex AI.

Here is an example of the output of a detected table using GPT-4o. Note specifically the text field that is added. Line breaks have been inserted here for readability. The output will not contain these line breaks.

{
    "type": "Table",
    "element_id": "5713c0e90194ac7f0f2c60dd614bd24d",
    "text": "The table consists of 6 rows and 7 columns. The columns represent 
        inhibitor concentration (g), bc (V/dec), ba (V/dec), Ecorr (V), icorr 
        (A/cm\u00b2), polarization resistance (\u03a9), and corrosion rate 
        (mm/year). As the inhibitor concentration increases, the corrosion 
        rate generally decreases, indicating the effectiveness of the 
        inhibitor. Notably, the polarization resistance increases with higher 
        inhibitor concentrations, peaking at 6 grams before slightly 
        decreasing. This suggests that the inhibitor is most effective at 
        6 grams, significantly reducing the corrosion rate and increasing 
        polarization resistance. The data provides valuable insights into the 
        optimal concentration of the inhibitor for corrosion prevention.",
    "metadata": {
        "text_as_html": "<table>...<full results omitted for brevity>...</table>",
        "filetype": "application/pdf",
        "languages": [
            "eng"
        ],
        "page_number": 1,
        "image_base64": "/9j...<full results omitted for brevity>...//Z",
        "image_mime_type": "image/jpeg",
        "filename": "7f239e1d4ef3556cc867a4bd321bbc41.pdf",
        "data_source": {}
    }
}

The generated table’s summary will overwrite any previous contents in the text field. The table’s original content is available in the image_base64 field.

Any embeddings that are produced after these summaries are generated will be based on the new text field’s contents.

Generate table descriptions

To generate table descriptions, in an Enrichment node in a workflow, specify the following:

You can change a workflow’s table description settings only through Custom workflow settings.

Table summaries are generated only when the Partitioner node in a workflow is also set to use the High Res partitioning strategy. Learn more.

Select Table, and then choose one of the following provider (and model) combinations to use:

• OpenAI (GPT-4o). Learn more.
• Anthropic (Claude 3.5 Sonnet). Learn more.
• Amazon Bedrock (Claude 3.5 Sonnet). Learn more.
• Vertex AI (Gemini 2.0 Flash). Learn more.

Make sure after you choose the provider and model, that Table Description is also displayed. If Table Description and Table to HTML are both displayed, be sure to select Table Description.